Deep Learning and NLP: Seq2Seq Model Theory + ChatGPT Prizes

Explore the plan of attack for intuition in deep NLP, covering NLP types, deep learning, bag of words, end-to-end models, sequence to sequence model, beam search, and attention.

Explore the three-part landscape of natural language processing through Venn diagrams, distinguishing NLP, deep learning, and deep NLP, and focus on sequence-to-sequence models as the cutting edge.

Compare natural language processing approaches with deep learning models, survey examples from if else rules to convolutional networks, and preview sequence to sequence models for deep NLP applications.

Explore end-to-end deep learning models in natural language processing and see why sequence to sequence models integrate transcription and meaning for better customer support interactions.

Learn how a bag-of-words model converts text into 20,000-word vectors, trains on yes/no email responses, and compares logistic regression with neural networks in deep NLP.

Explore seq2seq architecture and how recurrent neural networks overcome bag-of-words limitations by enabling variable input and output through many-to-many sequences, with word encoding using start-of-sentence and end-of-sentence tokens.

Explains a seq2seq architecture with an encoder-decoder using recurrent neural networks and LSTMs to encode text into a meaning vector and decode responses, including end-of-sentence termination and training basics.

Sequence-to-sequence training uses an encoder-decoder architecture, backpropagation, and stochastic gradient descent to adjust weights for variable input and output lengths.

Explore beam search decoding versus greedy decoding in sequence-to-sequence models, compare joint beam probabilities, and learn truncation techniques to manage beam explosion.

Explore attention mechanisms in seq2seq models, where the decoder queries encoder states to form a context vector via softmax-weighted sums, improving handling of long inputs and translation tasks.

See how attention weights guide English to French translation, and how global versus local attention improves translation of long sentences.

Begin data preprocessing for seq2seq models, laying the groundwork for effective deep learning and natural language processing experiments in this course.

Import numpy as np, TensorFlow as tf, re for text cleaning, and time to prepare a chatbot dataset in deep nlp, and verify installation before starting data preprocessing.

Load and prepare the dataset for seq2seq modeling by importing movie lines and conversations, handling utf eight encoding and setting errors to ignore, and mapping line IDs to text.

Map each line to its id to build an inputs–outputs dataset for seq2seq training, emphasizing dictionary-based wiring and data cleaning for smoother neural chatbot learning.

Learn how to preprocess conversation data by extracting line IDs, cleaning brackets and quotes, and building a large list of conversations to prepare inputs and targets for a seq2seq model.

Perform data preprocessing for seq2seq models by separating questions and answers into aligned inputs and targets, then prepare for cleaning with lowercase normalization.

Develop a clean_text function in Python to preprocess text for NLP chatbot training, lowering case, removing apostrophes, and standardizing contractions on questions and answers from the Cornell Movie Dialogue Corpus.

Apply the clean_text function to questions and answers, creating clean_questions and clean_answers. Proceed to remove non-important words in the next preprocessing steps for natural language processing.

Create a word frequency dictionary to identify and remove rare words, counting occurrences across questions and answers, and prepare data for efficient seq2seq training.

Builds two word-to-integer vocabularies for questions and answers by tokenizing and filtering rare words with a threshold of 20, mapping each word to a unique integer for seq2seq.

Add final tokens, including pad, EOS, out, and SOS, to the question and answer dictionaries for the seq2seq encoder and decoder. Create the inverse dictionary for answers.

Learn to invert the answers word-to-integer dictionary to obtain an integer-to-word mapping for the seq2seq model, using a Python trick and adding the end-of-string token for decoding.

Add an end-of-string token to every cleaned answer through an index loop, then prepare data by removing rare words and converting text to integers for length-based sorting.

Translate questions and answers into integers using existing dictionaries, handling unknown words with an out token, to create questions_to_int and answers_to_int and sort by length for faster training.

Sort questions and answers by the length of the questions to speed training and reduce padding, aligning data for a seq2seq model, and prepare for TensorFlow encoding and decoding.

Explore seq2seq model theory and practical steps to build a seq2seq model in deep learning and NLP as part 2 of the course.

Build the seq2seq model architecture in TensorFlow by creating input and target placeholders, plus learning rate and keepprob, via a dedicated model_input function.

Preprocess targets into batches and add the SOS token to each to feed the decoder in a seq2seq model. Implement batch creation and SOS token prep with TensorFlow.

Build the seq2seq model by creating the encoder RNN with stacked LSTM layers and dropout in TensorFlow, then prepare the decoder RNN.

Develop the decoder RNN in a seq2seq model by decoding the training set, then the validation set, using embeddings, attention, and dropout in TensorFlow.

Builds a seq2seq decoder for test and validation sets using attention-based inference, introducing sos and eos tokens, max length, and cross-validation to improve predictions.

Build the decoder RNN for a seq2seq model with a multi-layer LSTM and dropout, using the encoder state, embeddings, and a fully connected output layer to produce training predictions.

Assemble the encoder and decoder RNNs to build the seq2seq model, train on inputs and preprocessed targets with embeddings, and generate training and test predictions for a chatbot brain.

Explore practical steps to train the seq2seq model in part 3 of the course, covering architecture within deep learning and NLP for the ChatGPT prizes.

Reset the TensorFlow default graph and define an interactive session to prepare for training a seq2seq model. Load the model inputs using the function model input defined in part two.

Load the seq2seq model inputs with the custom model input function, returning inputs, targets, learning rate, and keep probability for the upcoming training step.

Set a maximum sequence length of 25 and create a sequence length variable via a TensorFlow placeholder with default to guide the encoder and decoder RNNs in seq2seq training.

Set hyperparameters and prepare the seq2seq model training by creating an interactive session, loading inputs, defining sequence length, and determining the input shape for training and test predictions.

Learn how to compute training and test predictions for a seq2seq model, feeding inputs and targets, and configuring batch size, sequence length, and embeddings in a TensorFlow workflow.

Set up a training scope with weighted cross-entropy loss and an Adam optimizer; apply gradient clipping to [-5, 5] to prevent exploding or vanishing gradients during seq2seq training.

Apply padding to batched sequences using the pad token, ensuring all questions and answers share the same length by computing the max sequence length and implementing an apply_padding function.

Split the data into batches of questions and answers, pad with pad tokens, and convert to numpy arrays for training; prepare a training and validation split for cross-validation.

Split questions and answers into training and validation sets, forming training questions, training answers, validation questions, and validation answers. Apply cross-validation with a 15% split to monitor seq2seq training.

Train a seq2seq model by tracking training loss every 100 batches, evaluating validation loss at epoch midpoints and end, and apply early stopping to save chat bot weights.

Apply learning rate decay with a minimum learning rate, monitor average validation loss for early stopping, save the best model with a saver, and continue training toward stopping criteria.

Explore testing seq2seq models in deep learning and natural language processing, focusing on evaluation methods and practical insights from part 4 of the series.

Test the seq2seq model by loading checkpoint weights, establishing a TensorFlow session, and restoring the chatbot’s weights so you can chat with the bot in the console.

convert the questions from strings to a list of encoding integers using the word-to-int dictionary and the clean text function, with out tokens for rare words to prepare chat.

Test a seq2seq chatbot by encoding input, padding to length 20, batching, running the model, and post-processing predicted tokens into a readable response.

Explore testing of a seq2seq chatbot using the sector model wrapper, including setting up a TensorFlow 0.10.1 environment, data preprocessing, and evaluating conversational performance.